Object tracking system

ABSTRACT

A system for tracking objects across an area having a network of cameras with overlapping and non-overlapping fields of view. The system may use a combination of color, shape, texture and/or multi-resolution histograms for object representation or target modeling for the tacking of an object from one camera to another. The system may include user and output interfacing.

BACKGROUND

The invention pertains to monitoring and particularly to camera-basedmonitoring. More particularly, the invention pertains to trackingobjects across networks of cameras.

SUMMARY

The invention includes camera networks for tracking objects acrossvarious fields-of-view and a processor for noting the tracking of theobjects within its field-of-view.

BRIEF DESCRIPTION OF THE DRAWING

FIG. 1 is an overview of a multi-camera based tracking system;

FIG. 2 shows components of an example user interface;

FIG. 3 shows the basic components of an image processor;

FIG. 4 reveals a basis of object manipulation having a histogram,multi-resolution and object representation;

FIG. 5 is a diagram of the manager module having threads and acoordinator;

FIG. 6 shows a video capture having a plurality of grabbers;

FIG. 7 is a flow diagram for tracking an object (e.g., target model) inanother or same camera field-of-view;

FIG. 8 is a flow diagram for computing a feature vector;

FIGS. 9, 10, 11 and 12 show a feature representation for differentimages of the same person;

FIG. 13 shows multi-resolution representative curves for the images ofFIGS. 9-12;

FIG. 14 is a graph of a two-dimensional probability density function ofa tracked object in pixel coordinate space;

FIGS. 15 a and 15 b show an example of background subtraction of astatic scene;

FIGS. 16 a and 16 b show an example of background subtraction of a sceneinvolving a non-static object;

FIG. 17 shows a subtraction technique for separating a foreground regionfrom a background of a scene;

FIGS. 18 a and 18 b are graphs showing an effect of backgroundsubtraction from a scene having a target object;

FIG. 19 shows a multi-resolution histogram of a target object;

FIG. 20 shows a flow diagram for a multiple-channel, multi-resolutionhistogram;

FIG. 21 shows an image of a person being tracked among another person;

FIG. 22 illustrates a particle filtering process using sub-windows;

FIG. 23 shows graphs of a score for various sub-windows in FIG. 22 ofthe image in FIG. 21;

FIGS. 24 a and 24 b reveal subtraction of background for a selectedimage patch;

FIG. 25 reveals a series of image frames at a location for tracking anobject or objects;

FIGS. 26 a and 27 a show selected frames from the series of frames inFIG. 25;

FIGS. 26 b and 27 b show a target patch of the frame in FIG. 26 a and aselected image of the frame in FIG. 27 a, respectively;

FIG. 28 shows a matching score for same size sub-windows of the frameshown in FIG. 27 a;

FIGS. 29 a, 29 b, 30 a and 30 b show image examples for evaluation of amulti-resolution histogram; and

FIGS. 31 a and 31 b show images having a histogram and particles shownas rectangles of a tracking task.

DESCRIPTION

Effective use of camera-based monitoring and surveillance systems mayrequire continuous (i.e., temporal) tracking of objects across networksof cameras with overlapping and/or non-overlapping fields-of-view(FOVs). Practical reasons for deploying these systems, especially thoseused for object tracking in large areas, may limit the number of camerasto be deployed. Furthermore, in order to maximize the coverage area ofuseful tracking, the cameras may be positioned with non-overlappingFOVs. Additionally, strict security requirements favor surveillancesystems that may have the location of the object being tracked (e.g., aperson at an airport) at all times. The present system may relate to theissue of continuous object tracking across a network of cameras with orwithout overlapping fields-of-view.

The present system may incorporate a Bayesian methodology regarded assome sort of sequential Monte-Carlo (SMC) approach. An SMC approach mayprovide a solution to the problem of image-based tracking throughstatistical sampling. As a result, this tracking approach may cope withscenarios in which object tracking lasts for as long as the objectremains in the FOV of a camera, stops while it is outside of the FOV,and automatically resumes when the object reappears in the camera's FOV.The present system may use a combination of both color and shapeinformation of the object to be tracked.

Tracking across two or more cameras may be achieved according to thefollowing. Tracking may be initiated within a first camera manually orvia a user input or automatically which last while the object beingtracked is within the first camera's FOV. Object information may besimultaneously communicated to other cameras which are in thetopological proximity of the first camera. Tracking tasks in the othercameras may be initiated and put into a mode as if the object haddisappeared from the other cameras' FOVs waiting to resume tracking whenthe object appears again in their FOVs.

To implement and use the present system, a list of cameras may bearranged according to the potential travels or routes that people ormoving objects of interest follow during their typical course of movingactivity. Based on a camera arrangement, a notion of topologicalproximity may thus be ascribed. One or more computers may be deployedfor processing camera images. Computing resources per camera may beallocated in a predefined or adaptive way. A tracking task of a movingobject may be initiated by a single click with an object's silhouette. Amotion detection procedure may be used to derive the color and shaperepresentation of a moving object. If the object is not moving, arectangle that encompasses the object of interest may be used. A threadimplementing SMC tracking may begin with a camera's FOV. As the objectmoves towards the camera's image boundaries in a particular direction,the camera(s) which is (are) in the topological neighborhood may beconditioned to expect an arrival of the object started to be tracked.Camera conditioning may mean that another SMC tracking is spawned usinga representation of the object provided by the previous tracking and soon.

The system may use a combination of color and shape for an objectrepresentation. The specific object representation may be embedded on anSMC framework for tracking. Topological arrangement of cameras coveringa large area may be based on the potential routes or paths of movingobjects. Computing resources allocated to the cameras may be based on aQuality of Service (QoS) concept which is derived from the topologicalproximity among network cameras.

FIG. 1 shows an overview of a system 10 that implements the presentinvention. There may be a user interface 11 that processes inputs froman operator to various modules of the system. An output from the userinterface 11 may go to a manager module 12, an image processor module13, a manipulator module 14, a direct X module 15, and an MFC 6.0 module16.

The user interface 11 may have inputs from a Safeskies™ user interfacesub-module 21 and a Safeskies™ test user interface sub-module 22, asshown in FIG. 2.

User interface sub-module 22 may be used for quickly testing differentmodules of system 10. The sub-module 22 may be utilized for exposing thesystem's capability and by minimizing the processing overhead. The userinterface sub-module 21 may be implemented when the modules are readyand debugged using a plug-in framework.

The image processor module 13 may have a background subtractorsub-module 23 and a particle filter sub-module 24 connected to it asshown in FIG. 3. This module covers particle filtering and modeltracking. The background subtractor sub-module 23 may be used for movingobject detection in a scene

The manipulator module 14 may implement an appearance model of theobject, such as color and multi-resolution histograms. FIG. 4 shows ahistogram sub-module 25 which may be connected to the multi-resolutionsub-module 26. Multi-resolution sub-module 26 may be connected to anobject representation sub-module 27, which in turn is connected to themanipulator module 14. The histogram sub-module 25 may implement ahistogram in various color spaces, such as the red, green and bluecombination, HSV and so on. The multi-resolution implementation maysupplement representation of objects in a scene. The objectrepresentation sub-module 27 may interface with a particle filter for acorrelation of particles with a target model.

The manager module 12 may have a threads sub-module 28 that mayimplement multiple threads associated with every camera node thatcomposes the camera network, as shown in FIG. 5. There may be one threadspecific to one camera. There may be one thread specific to one personfor one camera and multiple threads for the same person on variouscameras. There may be multiple threads for different people on onecamera. A coordinator sub-module 29 may be responsible for coordinatingthe exchange of information between the appropriate threads. The managermodule 12 may indicate which module goes next, whether it besequentially, temporally, or otherwise.

The direct X module 15 may be an interface for connecting to digitalcameras. The MFC 6.0 module 16 is for interfacing with certainMicrosoftT™ software.

The manager 12 module may have a connection to image processor module 13which in turn has a connection to a MIL 7 module 18. Manager 13 mayinclude a camera selector of a network of cameras covering a given area.These cameras might or might not have over-lapping FOVs. Module 18 is aninterface enables a direct connection with cameras. Image processormodule 13 is also connected to manipulator module 14.

Manager module 12 is connected to a video capture module 17. Videocapture module 17 may have a video grabber sub-module 31 whichfacilitates grabbing of image frames for processing. It is for commoncamera hook-ups. Module 17 may have a Mil grabber 32 which supports theMil system for analog cameras. Image frames may be captured either byframe grabbers (such as MIL grabbers) or digitally via a USB or firewire connection. Additionally, the sub-modules of module 17 mayfacilitate processing of video clips or composite image frames such asquad video coming from four cameras. A DS video grabber sub-module 34may be a part of module 17. Sub-module 34 may be a direct showconnection for a digital interface, in that it will permit the capturingof images from digital media. There may be a quad grabber sub-module 33.

FIG. 7 is a flow diagram 40 of the approach used by system 10 fortracking a person and finding that person in another or same camerafield of view. The system may be initialized at point 41 and an objectof interest to be tracked may be selected at item 30. A decision 42 maybe made as to whether the same camera, provided that the object has beena subject of tracking before this juncture, or the initial camera is tobe used. If so, then the target model of interest object may begenerated at point 43. If not, then the target model of the trackedobject may be obtained from the initially used camera at point 44. Aprior noted difference between the initial camera and another camera maybe incorporated from information 45 for compensation purposes of theother camera. The target model at point 46 may be transferred to themain stream particle or frame processing after camera compensation. Atpoint 47, either the generated target model from point 43 or thetransferred target model may enter the processing stream for initialparticle and weight generation. The next time's particle may bepredicted at point 48. At point 49, the calculation of each particle'sassociated feature such as color, shape, texture, and so forth, mayoccur. After the calculation, candidates may be formed from the featuresof the particles at point 50. Then a matching between the target andcandidate sets may occur at point 51. The match score may be used toupdate the particle and weight at point 52. This updated information maybe reported for recording or storage at point 53. Here, the weightedparticle's location may be summarized as tracking results which arereported. The update particle and weight information may go from point52 to point 54 where the target models may be updated. The updatedtarget model data may enter the processing stream at point 48 where thenext time's particle is predicted by the dynamic model. The process maycontinue reiteratively through points 49-54.

The tracking algorithm of the present system 10 may use histograms. Onefeature representation of the object may be a color histogram of theobject. The color histogram may be computed effectively and achieversignificant image data reduction. These histograms, however, may providelow level semantic image information. To further improve trackingcapabilities of an object or target, a multi-resolution histogram may beadded to obtain texture and shape information of the object. Themulti-resolution histogram may be a composite histogram of an imagepatch (or particle) at multiple resolutions. For example, to compute amulti-resolution histogram of an image patch, a multi-resolutiondecomposition of the image patch may be first obtained. Image resolutionmay be decreased with Gaussian filtering. The image patch at eachresolution k may give a different histogram h_(k). A multi-resolutionhistogram H may then be constructed by concatenating the intensityhistograms at different resolutions H=[h₀, h₁, h₂, . . . h_(j-1)].

Multi-resolution histograms may add robustness for tracking an objectrelative to noise, rotation, intensity, resolution and scale. Theseproperties may make the system a very powerful representation tool formodeling the appearance of people when considered in tracking.

FIG. 8 shows a flow chart 60 usable for computing a multi-resolutionhistogram feature vector. At point 61, a level of resolution may beentered at an input of the system. Then the Burt-Adelson Image Pyramidwith a Gaussian filter 5*5, at point 62. At point 63, histograms may becomputed for all of the designated levels of resolution of the images ofthe object. These histograms may be normalized by an L1 norm at point64. A cumulative histogram may be formed of all of the level resolutionimages of the object at point 65. At the next point 66, differencehistograms may be computed between the histograms of consecutive imageresolutions. The histogram of the original image may be discarded. Thenthe difference histograms may be concatenated to form the feature vectorat point 67.

One may note the performance of multi-resolution histograms of aperson's body parts, (i.e., upper and lower body and head). The sameperson viewed by a camera at different positions, orientations, scalesand illuminations is shown in FIGS. 9-12. A comparison of themulti-resolution histograms or feature vectors is shown in FIG. 13).

From a theoretical point of view, the difference histograms may relateto the generalized Fisher information measures, as described in thefollowing formulas.${J_{q}(I)} = {\frac{\sigma^{2}}{2}{\sum\limits_{j = 0}^{m - 1}{\left( \frac{v_{j} - v_{j}^{q}}{q - 1} \right)\frac{\mathbb{d}{h_{j}\left( {I*{G(1)}} \right)}}{\mathbb{d}1}}}}$where / is the intensity image, /(x) is the intensity value at pixel x;G(/) is Gaussian filter, / is the resolution, /*G(/) means filteredimage; $\frac{\mathbb{d}{h_{j}\left( {I*{G(l)}} \right)}}{\mathbb{d}l}$is the difference histogram between consecutive image resolutions; V_(j)is the value of histogram density j, and J_(q)(/) is the generalizedFisher information, which is proportional to the difference histogram.

FIG. 9 shows a detail feature representation for a first image. Column Ashows a multi-resolution decomposition by the Burt-Adelson imagepyramid, with level 0˜4 level. Histograms of the multi-resolution imagesfor each level are shown in column B. Column C reveals cumulativehistograms of the multi-resolution images for each level. Differencehistograms of the consecutive multi-resolution levels are revealed incolumn D. The difference histogram shows the transformation of the rateat which the histogram changes with image resolution into thegeneralized Fisher information measures. One may see that the differencehistogram also shows that the generalized information measures link atthe rate at which the histogram changes with image resolution toproperties of shapes and textures.

FIG. 10 shows a detail feature representation for a second image of thesame person as in the first image but with a different pose andillumination. Column A shows a multi-resolution decomposition by theBurt-Adelson image pyramid with level 0˜level 4. Column B reveals ahistogram of the multi-resolution images for each level. Cumulativehistograms of the multi-resolution images for each level are shown incolumn C. Column D reveals the difference histograms of the consecutivemulti-resolution levels.

FIG. 11 shows a detail feature representation for a third image of thesame person in the first and second images, but with a different poseand illumination. Column A shows a multi-resolution decomposition by theBurt-Adelson image pyramid with level 0˜level 4. Column B reveals ahistogram of the multi-resolution images for each level. Cumulativehistograms of the multi-resolution images for each level are shown inColumn C. Column D reveals the difference histograms of the consecutivemulti-resolution levels.

FIG. 12 shows a detail feature representation for a fourth image of thesame person in the first, second and third images but with a differentpose and illumination. Column A shows a multi-resolution decompositionby the Burt-Adelson image pyramid with level 0 level 4. Column B revealsa histogram of the multi-resolution images for each level. Cumulativehistograms of the multi-resolution images for each level are shown incolumn C. Column D reveals the difference histograms of consecutivemulti-resolution levels.

FIGS. 9-12 show the multi-resolution histogram representation for thesame person under a different pose, scale and illumination. Theconcatenate difference histogram is shown in FIG. 13. This figurereveals that the four curves for the four images to be so similar as tobe identifying the same person. Curves 71, 72, 73 and 74 aremulti-resolution representations of the first, second, third and fourthimages, respectively.

One may continue to build on the representation methodology bydiscussing the matching process for determining the similarity andultimately the match between two different object representations. Twosteps in particle filter tracking may be, first, a prediction step (thatpredicts the change of state of the target, i.e., position and size ofthe target object); and, second, a measurement step, i.e., imagemeasurements that facilitate the process of building confidence aboutthe prediction regarding the target at hand. The object representationand related matching algorithm are essential items of the measurementapproach.

A matching algorithm may first use color information to form objectrepresentations. An object representation may be a weighted histogrambuilt using the following formula.q _(u) =CΣ _(i) f(δ[b(x _(i))−u])  (1)The candidate object representation may be given by a similar weightedcolor histogram as shown in the following formula.p _(u)(s _(t) ^((n)))=CΣ _(i) f(δ[b(x _(i))−u])  (2)In target representation (1) and candidate object representation (2), Cis normalization factor, and f(·) may be a kernel function to weightmore on the center of the region. Then the matching algorithm may usethe following distance function which is given by the followingBhattacharyya coefficient (3).m(p_(u)(S_(t) ^((n))),q_(u))  (3)The smaller the distance between the target model and candidate region,the more similar the regions are. The relationship of the matchingdistance function in the measurement step in the particle filteringtracking algorithm may be given by the following formula (4),π_(t) ^((n)) =p(Z _(t) |x _(t) =s _(t) ^((n)))=m(p _(u)(S _(t) ^((n))),q_(u))  (4),where the weighted sample set(S _(t) ^((n)),π_(t) ^((n))),n=1, . . . , NN represents the number of particles used for tracking. Particles may beused to approximate the probability distribution of the target statethat the tracking algorithm predicts. A visualization of the respectiveprobability density function is shown in FIG. 14. FIG. 14 shows atwo-dimensional probability density function “p(.)” 75 of a trackedobject in pixel coordinate space.

For target representation, one may use a multi-resolution histogramrather than the scheme described in formulae (1) and (2) above. Amulti-resolution histogram may include color, shape and textureinformation in one compact form.

Background subtraction may be shown in FIGS. 15 a, 15 b, 16 a and 16 b.FIG. 15 a shows a background 99 of a laboratory scene. FIG. 15 b revealsa background learning of static scene of the laboratory background 99.FIG. 16 a shows a non-static person 102 in the laboratory scene. FIG. 16b distinguishes the non-static person 102 from the background 99. Itamounts to moving object 102 detection via background 99 subtraction.

FIG. 17 shows an approach where a background subtraction technique maybe applied to separate the foreground region 76 (i.e., the person) fromthe background 77 before a multi-resolution histogram is applied. Byperforming a foreground/background subtraction 78 and a head and upperbody selection 79 first, a more robust representation of a person may beassured because the effect of the background which is considered noiseis minimized. The effect of background subtraction 78 on the targetobject 76 representation is shown in FIGS. 18 a and 18 b. These figuresreveal an effect of background subtraction 78 on the target object 76representation. FIG. 16 a shows a plain color histogram 81 of the wholeimage with the background 77 and FIG. 18 b shows a masked colorhistogram 82 with the background subtracted out of the image. One maynote that histogram 82 of only the foreground target object or person 76appears to be better distributed than histogram 81 where the background77 is included. Background 77 appears as another mode 83 in the firstbin of histogram 81 in FIG. 18 a.

FIG. 19 shows a multi-resolution histogram of the target object 76.Column A shows a multi-resolution decomposition by the Burt-Adelsonimage pyramid with level 0˜level 4. Column B reveals a histogram of themulti-resolution images for each level. Cumulative histograms of themulti-resolution images for each level are shown in column C. Column Dreveals the difference histograms of consecutive multi-resolutionlevels. The multi-resolution histogram shown may be performed on theperson 76 that was separated from the background 77. The cumulativehistogram of column C may be used here. For instance, one may want tocompare example histogram as follows,

hA=(1; 0; 0), hB=(0; 1; 0), and hC=(0; 0; 1). Histogram hA may be moresimilar to histogram hB, than histogram hA is to histogram hC. The L1distance, however, between hA and hB is the same as the L1 distancebetween hA, and hC. That is,|hA−hB|1=|hA−hC|1.Therefore, the histograms in their original form do not necessarilyrepresent the fact that hA is more similar to hb than hA is to hC. Thecorresponding cumulative histograms are hcum_A=(1; 1; 1), hcum_B=(0; 1;1), and hcum_C=(0; 0; 1). The distances between the cumulativehistograms satisfy:|h_(cum) _(—) _(A)−h_(cum) _(—) _(B|)1<|h_(cum) _(—) _(A)−h_(cum) _(—)_(c)|₁as one may expect.

FIG. 20 shows a flow diagram for developing a 3 channel (HSV)multi-resolution histogram for color space. From an RGB image 84, onechannel 85 may be for hue, a second channel 86 for saturation, and athird channel 87 for value, as examples. Multi-resolution histograms 88,89 and 91 may be made for channels 85, 86 and 87. These histograms maybe linked or concatenated.

Matching image regions may be important during tracking (althoughmatching is not equivalent to tracking). In a particle filter framework,the matching performance may greatly affect the measurement step. FIG.21 shows an image of the person 76 being tracked appearing at adifferent pose, position and orientation than the pose, position andorientation of the person 76 when being initially tracked. Additionally,another person 92 is present in image picture of FIG. 21. The particlefiltering process may be emulated by examining the image at fixed sizesub-windows 93 thereby sweeping the whole image for the presence of theappropriate target 76, in FIG. 22. The original representation of thetarget 76 was performed on an image different from the one of appearingin FIG. 21.

FIG. 23 shows a matching score for the various sub-windows 93 of theimage appearing in FIG. 21. Without any background subtraction theselected person or target 76 (the sub-window 93 with the smallestdistance) is shown as an image patch in FIG. 24 a. It may be noted thateven under a fixed sub-window, the matching algorithm reveals goodperformance. However, when background 77 subtraction is added, and thesame steps are followed for the selected image patch shown in FIG. 24 b,superior performance may be verified.

FIG. 25 shows a sequence of 25 consecutive frames taken at an airport.Here, one may note an example of using multi-resolution histograms forpeople tracking with a video sequence from the airport. Frames 80 and 90may be of particular interest. There may be a temporary gap betweenimage from 80 and image frame 90, also shown in FIGS. 26 a and 27 a,respectively. In frame 90, there is a person 94 shown in the upper leftcorner of frame 90, who does not appear shown in frame 80. So there maybe a degree of difficulty introduced in matching a cropped target patch95 and candidate patches along with a change in illumination. Bymatching the multi-resolution histogram of the target on candidateregions in the image frame 90, an image patch 96 is a selected region ashaving a similar or matching histogram. Target patch 95 of frame 80 andselected image patch 96 of frame 90 are shown in FIGS. 26 b and 27 b,respectively. FIG. 28 shows a matching score on the same size sub-windowon the image frame 90. It may be noted that for the selected image patch96 in FIG. 27 b, it gives the smallest L1 distance when compared withthe original target image patch 95 of FIG. 26 b, as shown in FIG. 28which shows a nearly matching score on the same size sub-windows offrame 90.

FIGS. 29 a, 29 b, 30 a and 30 b represent a scenario of evaluatingperformance of a multi-resolution histogram representation onprerecorded video sequences that may represent increasingly complextracking scenarios. In FIG. 29 a, the image may be a frame number 101 ina video sequence. Using this image, one may first crop a target patch 97including one person, as shown in FIG. 29 b. The image of FIG. 30 a maybe a frame number 110 in the same video sequence. By using and matchingmulti-resolution histograms on the candidate regions in the image frames101 and 110, one may get a selected image patch 98 from image frame 110,as shown in FIG. 30 b. It is evident that the person selected out of thelatter image, based on a matching histogram, is the same person in thetarget patch. This selection reveals good performance ofmulti-resolution histograms for tracking in a crowded scenario.

FIG. 31 a shows a target patch 103 on a person 104 in a relativelypopulated area, such as an airport. A color histogram may be taken ofthe patch 103. FIG. 31 b reveals particles 105 shown as rectanglesduring a tracking task using a particle filter deploying the colorhistogram representation.

In the present specification, some of the matter may be of ahypothetical or prophetic nature although stated in another manner ortense.

Although the invention has been described with respect to at least oneillustrative example, many variations and modifications will becomeapparent to those skilled in the art upon reading the presentspecification. It is therefore the intention that the appended claims beinterpreted as broadly as possible in view of the prior art to includeall such variations and modifications.

1. A tracking system comprising: a user interface; a manager connectedto the user interface; an image processor connected to the userinterface; an image manipulator connected to the user interface and theimage processor; and a video capture mechanism connected to the manager.2. The system of claim 1, further comprising: a threads module connectedto the manager; and a coordinator connected to the manager.
 3. Thesystem of claim 1, further comprising: an object representation moduleconnected to the manipulator; a multi-resolution module connected to theobject representation module; and a histogram module connected to themulti-resolution module.
 4. The system of claim 1, further comprising aplurality of video grabbers connected to the video capture mechanism. 5.The system of claim 1, further comprising: a background subtractionmodule connected to the image processor; and a particle filter connectedto the image processor.
 6. A method for tracking an object comprising:initializing tracking; selecting a camera; obtaining a target model of atracked object; generating a first particle; predicting a next time'sparticle by a dynamic model; and calculating features of the firstparticle.
 7. The method of claim 6 further comprising: formingcandidates from the features; matching the target model with thecandidates; updating the particle; and updating the target model.
 8. Themethod of claim 7, wherein the updating the target model comprisesreporting tracking based on a location of the particles.
 9. The methodof claim 7, further comprising: changing to a second camera from a firstcamera; transferring the target model of the tracked object from thefirst camera to the second camera; generating a particle; predicting anext particle by the target model; and calculating features of theparticle.
 10. The method of claim 9, wherein the field of view does notoverlap with the field of view of the first camera.
 11. The method ofclaim 9, further comprising: forming candidates from the features;matching the target model with the candidates; updating the particle;reporting tracking based on a location of the particle; and updating thetarget model.
 12. The method of claim 11, further comprising repeatingthe method of claim
 10. 13. The method of claim 6, wherein a feature ofthe particle is determined by: computing histograms of various levelresolution images of the target model; normalizing the histograms;forming a cumulative histogram from the histograms of the various levelresolution images; computing difference histograms between thehistograms of consecutive various level resolution images; andconcatenating the difference histograms to determine the feature of theparticle.
 14. A tracking system comprising: a plurality of cameras; acamera selector connected to the plurality of cameras; and an imageprocessor connected to the plurality of cameras and the camera selector.15. The system of claim 14, wherein: each camera of the plurality ofcameras is situated at a location of an area; and the location of eachcamera is different from the location of another camera of the pluralityof cameras.
 16. The system of claim 15, wherein some of the cameras ofthe plurality of cameras have non-overlapping fields of view.
 17. Thesystem of claim 15, wherein the image processor comprises: a targetmodel generator; and a particle generator connected to the target modelgenerator.
 18. The system of claim 17, wherein the particle generatorcomprises a particle feature generator.
 19. The system of claim 18,wherein the image processor further comprises: a candidate generatorconnected to the particle generator; and a matcher connected to thecandidate generator and the target model generator.
 20. The system ofclaim 19, wherein the particle feature generator comprises a histogramgenerator.